A lightweight face classification tool that ensures real-time, energy-efficient performance with advanced occlusion handling and dynamic adaptability, Featuring Python-based APIs.
Type
Personal Project
Timeframe
1 Month
Toolkit
Python
Year
2022
Problem
Face classification systems mostly rely on cloud services, limiting scalability and adaptability in offline environments. Current solutions struggle with real-time inference, handling diverse conditions like occlusions or lighting changes, and providing lightweight, efficient models that perform seamlessly on low-resource devices.
Solution
This project delivers a lightweight face classification system using model quantization (TensorFlow Lite/ONNX) for optimized performance on edge devices. It offers real-time inference through asynchronous processing, dynamic adaptability via transfer learning, and robust handling of occlusions (e.g., masks, sunglasses). With energy-efficient processing and user-friendly Python APIs, the solution scales locally, ensuring privacy, speed, and adaptability in any environment.
To beat the monster, we had to draw the monster. That's why we dive into deep product analysis, detected and prioritized the following issues:
Getting more information about the target audience of business travelers and digital nomads made us suggest the Team change the direction of the product to become a mobile-first platform. A smartphone is a thing every person own so the product will spread faster and the platform engagement is going to be increased dramatically. And that's what we aiming to, right?
